Below are upcoming training opportunities for the fall that may be of interest to faculty and students. Most sessions are online and free of charge.

REGISTER: https://www.ace-net.ca/training/

Introduction to Machine Learning
2, 9, 16 November, 1200-1500hrs Atlantic | 1230-1530 NL (online)
These hands-on sessions guide learners through the end-to-end machine learning lifecycle using Python and Scikit-learn. Participants evaluate when to apply machine learning to real-world challenges and implement initial baseline approaches for straightforward datasets. Through interactive coding exercises, learners import data, preprocess features, and build, train and evaluate fundamental models. The curriculum contrasts key supervised learning algorithms including linear and logistic regression, decision trees, and random forests while introducing unsupervised methods like K-Means clustering to discover latent structures within unlabeled data. Learners quantify model performance using regression metrics like R2 and mean squared error alongside classification tools such as accuracy and confusion matrices.

Data Cleaning with OpenRefine
3 November, 1000-1300hrs Atlantic | 1030-1330hrs NL (online)
Researchers and librarians often deal with “messy” data. This could include inconsistent interview transcripts, poorly formatted longitudinal datasets, or standardizing names for an authority file. Preparing this data for analysis is a critical step that requires the same level of rigor and reproducibility as the analysis itself. This workshop introduces OpenRefine, a powerful, free, and open-source tool for cleaning, normalizing, and transforming research data. participants work with a sample dataset to learn how to efficiently handle common data issues, such as automating data standardization, identifying clusters of similar entries, and transforming complex strings into structured information without the need for advanced programming. Unlike manual editing in Excel, OpenRefine records every step of your process, ensuring your data cleaning is transparent and fully reproducible. Participants should be comfortable using a computer and have a general understanding of spreadsheets. No prior programming experience is necessary.

Introduction to R for Reproducible Scientific Analysis
17, 19 November, 1200-1500hrs Atlantic | 1230-1530hrs NL (online)
This hands-on, beginner level series covers the fundamentals of R, including data types, functions, importing, manipulating and analyzing data as well as program design, data management, and task automation. Participants are encouraged to help one another and to apply what they have learned to their own research problems. The goal is to teach the practical knowledge needed to start programming, debugging and using R in everyday tasks. You do not need any previous knowledge of the tools that are presented, or previous programming experience, but intermediate level experience with a computer is highly recommended.

Visualization with R
24 November, 1200-1400hrs Atlantic | 1230-1430 NL (online)
When managing large datasets, visual representation is essential for uncovering hidden patterns and communicating insights. This session focuses on transitioning from raw numbers to impactful graphics — such as histograms, scatter plots, and box plots — using the powerful R programming language. While basic R knowledge is helpful, this workshop is designed to be accessible to all. We move beyond simple charting to explore the "Grammar of Graphics" approach, teaching you how to build visualizations layer-by-layer. Participants should be familiar with R (variables, vectors, and data frames).

Introduction to Large Language Models (LLMs)
3 December, 1200-1500hrs Atlantic | 1230-1530hrs NL (online)
This comprehensive, non-coding session provides an architectural walkthrough of Large Language Models (LLMs), bridging the gap between high-level AI concepts and practical model utilization. The session traces the historical evolution of Natural Language Processing (NLP), tracking how early statistical models (N-grams, Bag-of-Words) and sequential deep learning networks transitioned into modern self-attention paradigms. Participants examine the inner workings of the Transformer architecture, exploring tokenization, embeddings, multi-head self-attention, and the structural trade-offs between encoder-only, decoder-only, and encoder-decoder designs, while analyzing how context window limits dictate memory consumption and computational complexity. Beyond core mechanics, the session evaluates the current LLM ecosystem through model benchmarks, comparing leading families (OpenAI, Anthropic, Google, Meta, and DeepSeek) across parameters like reasoning, cost efficiency, multimodal capabilities, and alignment. Finally, through guided visual demonstrations and concrete examples, participants discover how to access these models via proprietary APIs and open-source platforms (such as Hugging Face), identify key failure modes (hallucinations, bias, prompt injection), and learn some applied prompt engineering patterns to reliably direct model behaviour.